Group clothing intelligent size generation method and system based on multi-template dynamic adaptation

Abstract

The invention relates to the technical field of group clothing intelligent size generation based on multi-template dynamic adaptation, and discloses a group clothing intelligent size generation method and system based on multi-template dynamic adaptation. According to the scheme, firstly, the height and the weight of each member in a group are recorded in a standardized mode, then three template systems including a 5cm interval template, a 10cm interval template and a comprehensive template are set, and mapping functions and threshold values are set respectively; through the steps, the problems that in the prior art, data collection is not standard, template parameters are fixed, threshold setting is not clear, and size division is not fine are solved, so that size dynamic adaptation and standardized output are achieved, and the adaptation precision and production efficiency of the group garment size are remarkably improved; meanwhile, the situation that the sizes of the clothes are not uniform and individual differences cannot be fully reflected is improved, and finally the wearing comfort and attractiveness of the clothes are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent size generation for group clothing based on multi-template dynamic adaptation, and specifically to a method and system for intelligent size generation of group clothing based on multi-template dynamic adaptation. Background Art

[0002] Currently, in the field of clothing production and design, the size generation of group clothing mainly relies on traditional size charts and fixed template designs. In the prior art, most enterprises adopt a size division method based on static data, the core of which is to determine fixed height and weight intervals according to historical statistical data and generate a unified size chart accordingly. Although this method has certain applicability in the production of a single population and a small number of clothing styles, in the production of group clothing, due to the large differences in the body types of different members, it is easy to cause problems such as low individual matching degree and insufficient clothing wearing comfort.

[0003] Traditional size generation methods are mostly based on a single template or a simple piecewise function, mapping height and weight data to a predetermined size range. Generally speaking, the commonly used templates in the prior art include the 5-cm interval template or the 10-cm interval template, and their mapping formulas usually involve subtracting a fixed value from the height, dividing by a constant, and then using the floor function to determine the size level. Although this method is easy to operate, its fixed constant and piecewise method lack fine control over individual differences and tend to ignore the subtle deviations in body shape among members. At the same time, some technologies use a comprehensive template to simply linearly superimpose height and weight and then map, but this method fails to fully reflect the respective influencing factors of height and weight in clothing fitting and does not establish a perfect body shape correction mechanism. In the existing technical system, the generation of group clothing sizes mainly relies on empirical data and traditional statistical methods for grouping and classification. In terms of data collection, manual entry or basic measurement equipment is usually used for measurement, and data records are mostly stored in a simple numerical manner and statistically analyzed through a fixed format. This method has defects such as a high data entry error rate and inconsistent data formats, thus affecting the accuracy of size generation. For individual body shape deviations, the prior art mostly only uses static indicators for judgment, such as comparing the weight with a fixed standard weight, but fails to introduce a more intuitive mathematical model for dynamic classification and fine correction of body shapes. In addition, in terms of template mapping and dynamic adaptation rule formulation, the prior art usually uses pre-set fixed mapping functions and static threshold sets. For example, some technologies map height data to different size levels by setting fixed 5-cm or 10-cm interval thresholds, but this method cannot dynamically respond to the actual distribution of the body shape data of group members and is also difficult to meet the fine-tuning requirements for individual body shape deviations. In most systems, the template mapping function and body shape correction method use traditional mathematical methods or simple linear formulas and fail to fully consider the individual differences and diversity among group members in the actual process of wearing clothing.

[0004] Therefore, this case aims to propose an intelligent size generation method and system for group clothing based on multi-template dynamic adaptation, which adopts a completely new design in aspects such as template design, data collection, mapping function construction, body shape correction, personalized real-time fine-tuning, and final data output, aiming to make up for the deficiencies of the prior art in size dynamic adaptation, individual body shape classification, and data processing. This method ensures high accuracy and operability in the size generation of each group member through clear mathematical formulas and definite numerical parameters, and at the same time provides standardized and structured production guidance data for subsequent clothing production, thereby improving the production efficiency and wearing comfort of group clothing. Summary of the Invention

[0005] The present invention provides an intelligent size generation method for group clothing based on multi-template dynamic adaptation, which promotes the solution of the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solutions: An intelligent size generation method for group clothing based on multi-template dynamic adaptation, including: Denote the height of any member in the group as , and the value range is , with the unit of centimeter; Denote the weight of any member in the group as , and the value range is , with the unit of kilogram; Set template , and at the same time set the 5 cm interval template mapping function as ; Set template , and at the same time set the 10 cm interval template mapping function as ; Set template , and at the same time set the comprehensive template mapping function as ; For template , set the threshold set as: ; where each threshold corresponds to a 5 cm interval level; For template , set the threshold set as: ; where each threshold corresponds to a 10 cm interval level; Collect user data and then perform individual classification; Formulate template mapping and dynamic adaptation rules; Users perform template selection and personalized real-time size fine-tuning; Integrate and statistically generate group size data; Output the final data and production guidance documents.

[0007] Optionally, the setting of the 5 cm interval template mapping function as , the setting of the 10 cm interval template mapping function as and the setting of the comprehensive template mapping function as specifically include: Set the 5 cm interval template mapping function as , ; Among them, is the floor function; the denominator 5 represents the fixed interval of height difference; Set the 10 cm interval template mapping function as , ; where the denominator 10 represents a fixed interval of height difference; Set the comprehensive template mapping function as , ; where the denominator 10 is used to unify the mapping scale.

[0008] Optionally, the collection of user data and then individual classification is specifically as follows: Obtain the height of each group member; Obtain the weight of each group member; Record the data of each group member as: ; where is the unique identifier of the th member; is the height of the th member; is the weight of the th member; For the th member, set the reference weight , specifically as follows: ; where the denominator 2 is the standard constant for human weight gain; Set the classification mark to reflect the body type deviation of group members, specifically as follows: ; where is the classification mark of the th member.

[0009] Optionally, the template mapping and dynamic adaptation rule formulation are specifically as follows: Each group member calculates the preliminary size index according to the template selected by him / her, specifically as follows: ; where is the template selected by the th member; , indicating the selection of template ; , indicating the selection of template ; , indicating the selection of template ; is the preliminary size index of the th member; Set the body type correction function to calculate the adjusted size index, specifically as follows: ; Among them, is the adjusted size index of the th member.

[0010] Optionally, the user performs template selection and real-time personalized size fine-tuning, specifically: In the user operation interface, each member confirms the selected template type and size index ; Set the adjustment range in the size adjustment input box to be , where is the minimum value that can be entered in the size adjustment input box; is the maximum value that can be entered in the size adjustment input box; Obtain the fine-tuning value entered by the th member in the group through the size adjustment input box ; According to the user confirmation and fine-tuning, calculate the final size index, specifically: ; where is the final size index of the th member.

[0011] Optionally, the integration and statistics generation of the group size data is specifically: For the th member in the group, construct a record, specifically: ; And store all records in the database; Set the group size set to be ; where is the total number of group members; Calculate the average size of the group members, specifically: ; where is the arithmetic mean of the group member sizes; Obtain the minimum size among the group member sizes, specifically: ; where is the minimum size among the group member sizes; Obtain the maximum size among the group member sizes, specifically: ; where is the maximum size among the group member sizes; For any unique size , set a calculation function to calculate the number of occurrences, specifically: ; Wherein, is to count the records that meet the conditions; is the count of unique sizes .

[0012] Optionally, the output of the final data and the production guidance document are specifically: S61. Generate the final size details table: Construct a size details table, and the content of each row of records is: ; The output file adopts the comma-separated value format, and the first row is the title , and then sequentially sort the records according to the numbers of the group members from 1 to ; S62. Generate production quantity guidance information: For each unique size , generate production instruction data: ; Record the recommended production quantity corresponding to each size; S63. Compile a comprehensive production guidance document: The document includes: the data of the size details table; the group data , and ; the production quantity corresponding to each size ; the template mapping function , and ; S64. Data output interface specification: Specify that the output file format is a CSV file, and the first row is the fixed field title.

[0013] A system for implementing the intelligent size generation method of group clothing based on multi-template dynamic adaptation includes: A template management module for setting templates and template mappings; A rule engine module for classifying and marking height and weight data; A dynamic interaction module for users to input fine-tuning values and select template types and size displays; A data export module for generating the final size table and outputting production data.

[0014] The present invention has the following beneficial effects: 1. By first recording the height and weight of any member in the group and fixing their value ranges in the steps, with the height in centimeters and the weight in kilograms, this solution addresses the problems of non-standard data collection and unclear data ranges in the prior art. Subsequently, by setting up a multi-template system, including a 5-cm interval template, a 10-cm interval template, and a comprehensive template, each template is equipped with a clear mapping function. The mapping function of the 5-cm interval template maps the height to a size index through a fixed formula, solving the problem of coarse size division in traditional methods; the mapping function of the 10-cm interval template is more suitable for clothing adaptation for groups with relatively high or low heights with a fixed 10-cm interval; while the mapping function of the comprehensive template maps by combining the values of height and weight, unifying the mapping scale, thus addressing the deficiency that a single dimension cannot fully reflect individual body type differences. By setting a threshold set for the templates, where the threshold sets for the 5-cm and 10-cm intervals clearly correspond to their respective levels, this solution eliminates the problem of inaccurate size adaptation caused by unclear threshold settings and inflexible parameter adjustment in traditional technologies. At the same time, for the comprehensive template, the threshold is directly determined by the values in the formula without the need to separately set a threshold set, simplifying the system design and ensuring data consistency and standardization. Generally speaking, through standardized data collection, setting clear multi-template mapping functions, and strict threshold set settings, this solution addresses the problems of unreasonable clothing size generation and low individual matching degree in the prior art due to non-standard data, fixed template parameters, and inaccurate threshold settings, significantly improving the adaptation accuracy and production efficiency of group clothing sizes. At the same time, it provides accurate and standardized data output for subsequent production processes, effectively improving the problems of inconsistent sizes and inability to fully reflect individual differences in clothing production, thereby enhancing the comfort and aesthetics of clothing wearing.

[0015] 2. By obtaining the height and weight data of each group member and recording them as standardized data records containing unique identifiers, heights, and weights, this solution effectively addresses the issues of inconsistent data collection standards and unclear data formats in the prior art. First, the height and weight of each member are obtained, ensuring that each data item has a fixed value range and unit, thus avoiding deviations caused by measurement errors or irregular records. Next, the system formats the data record of each member into a record containing a unique identifier (identifying each member), height, and weight, making the entire data set have good structure and traceability, and solving the problem of difficult subsequent processing caused by chaotic data recording methods in traditional systems. On this basis, this solution sets a reference weight for each group member. This step constructs a standardized reference weight by using height data, providing a clear reference for subsequent individual classification. Through this calculation, the actual weight can be intuitively compared with the reference weight to determine the body type deviation of each member. Subsequently, the system further sets classification marks to qualitatively classify the body type of each member. The classification marks are based on the comparison result between the actual weight of the member and the calculated reference weight, clearly identifying whether the member is underweight, standard, or overweight. In this way, the problem of inaccurate size generation due to the lack of dynamic and clear classification criteria in traditional methods is effectively solved. The introduction of classification marks enables the system to implement refined management according to individual differences, thereby improving the accuracy and personalization level of size allocation. Generally speaking, through the four steps of data collection, standardized recording, reference weight calculation, and classification mark setting, this solution solves the problems of non-standard data, inconsistent records, lack of unified reference, and unclear individual body type judgment in the prior art. This step not only ensures the accurate collection and reasonable recording of each member's data, but also provides a reliable basis for subsequent size generation and template mapping, ultimately achieving the precise division and dynamic adaptation of group clothing sizes and improving the overall production and wearing effects.

[0016] 3. Through a series of steps including template mapping and dynamic adaptation rule formulation, this solution first requires each group member to select a predefined template type according to their own situation and calculate a preliminary size index using the corresponding template mapping function. This step solves the problems of fixed templates and inflexible size index calculation in traditional size generation methods. Specifically, by calling the template mapping function, the height of the member or the comprehensive data of height and weight is directly mapped to a preliminary size index, enabling each member to obtain a preliminary size value calculated based on a fixed formula. Since each template has clear mapping rules, such as differences in intervals for different templates, the system can perform more refined hierarchical mapping for different body type groups, thereby improving the accuracy of size calculation. Next, the solution sets a body type correction function to dynamically adjust the preliminary size index. This function combines the body type classification label obtained from the previous data collection stage with the preliminary size index and performs mathematical operations to generate an adjusted size index. This process solves the problem in traditional technologies that cannot fully reflect individual body type differences and dynamic deviations. Specifically, the body type correction function can perform addition or subtraction operations on the preliminary size index according to the actual body type deviation of the member, such as being thinner or fatter, so that the finally mapped size can reflect both the physiological parameters and individual body type characteristics of the member, achieving precise quantification of the size index. Overall, through template mapping and dynamic adaptation rule formulation, the solution solves the problems in traditional methods such as inaccurate individual size adaptation due to fixed templates, lack of flexibility in size index calculation, and insufficient consideration of body type deviation. By taking the steps of pre-selecting templates, using mapping functions to calculate preliminary size indices, and then combining body type correction functions for adjustment, it ensures that the physiological data and body type characteristics of each member can be accurately and directly mapped to a unique integer size index. This method not only improves the accuracy of size generation but also makes the size calculation process operable and adaptable, thus providing standardized and refined production guidance data for subsequent clothing production and effectively improving the defects of uneven traditional size distribution and insufficient adaptability.

[0017] 4. Through a series of steps including template selection and personalized real-time size fine-tuning, this solution enables users to further confirm and adjust the automatically generated size during the actual operation process, thus solving the problem that the size calculation results in traditional size generation methods do not fully match the individual's actual wearing needs. First, in the user operation interface, each group member needs to confirm the size index calculated in advance by the system based on template mapping and body type correction, and select the corresponding template type. This step ensures that each member can intuitively understand their preliminary size based on physiological data and conduct a preliminary verification, avoiding potential deviations that may occur by simply relying on the automatic calculation results. Secondly, the system sets up a size adjustment input box in the interface and clearly defines the adjustment range, such as the minimum and maximum values of the input range. On the one hand, this provides a safe and standardized fine-tuning space for users, and on the other hand, it prevents users from entering values outside the reasonable range, which may lead to system output errors. Next, the system real-time obtains the fine-tuning values input by each member of the group through the adjustment input box and adds the fine-tuning values to the preliminary size index to calculate the final size index. This calculation process uses direct numerical addition to ensure that all fine-tuning operations are based on clear and fixed values, thus avoiding errors caused by complex models or unclear weight settings in traditional technologies. Through this operation, users can directly affect the final size index according to their wearing experience, making the size generation both highly efficient in automation and retaining the flexibility and accuracy of personalized customization. In summary, by confirming the template type and preliminary size in the user operation interface, setting the numerical range of the size adjustment input box, real-time obtaining the fine-tuning values, and adding the fine-tuning results to the automatically calculated size index, this solution successfully solves the problems that the size calculation results in traditional systems cannot fully reflect individual needs and the lack of personalized customization ability. Finally, this step realizes an accurate size generation process combining automatic calculation and user real-time fine-tuning, which not only improves the accuracy of size data but also enhances the user's sense of participation and customization experience, providing more scientific and standardized production guidance data for subsequent clothing production and ensuring a significant improvement in the fit and wearing comfort of group clothing.

[0018] 5. Through this series of steps of integrating and statistically analyzing group size data, this solution first records the data of each member in the group in detail. The data record of each member includes a unique identifier, height, weight, template selection, and the finally generated size index. All data records are stored in a unified database, thus solving the problems of scattered data and inconsistent records in traditional methods. Next, the system sets up a group size set, aggregates the final size indexes of all members, and calculates the average size, minimum size, and maximum size of the group members, solving the problems of lack of overall data analysis and inability to reflect the balance of size distribution among group members in traditional methods. Specifically, calculating the average size can intuitively reflect the overall size level, and obtaining the minimum size and maximum size effectively reveals the differences among individuals, thus providing an intuitive statistical basis for subsequent production. In addition, by setting up a calculation function for any unique size and counting the number of times each size appears one by one, this solution solves the problems of inaccurate size statistics and unclear production quantity suggestions in traditional technologies. This calculation function counts the records that meet the conditions to ensure the accuracy and reliability of the production suggestion quantity for each size. Through this series of data integration and statistical steps, the system not only improves the accuracy and standardization of data processing, but also provides accurate size distribution data and production quantity guidance for the production link, thereby effectively reducing the waste of production resources and inventory risks caused by uneven size distribution. Generally speaking, by constructing group data records, storing them in the database, constructing a size set, calculating the average, minimum, and maximum sizes, and counting and statistics of unique sizes, the problems of scattered data collection, inaccurate statistics, and insufficient production guidance data in the existing technology are solved, making the group clothing size allocation more scientific and reasonable, providing a rigorous and reliable basis for clothing production, and ultimately improving the comfort of clothing wearing and the overall production efficiency.

[0019] 6. Through this series of steps including final data output and production guidance document generation, this solution realizes the seamless connection of information from data collection, analysis to production execution, and solves the problems of scattered traditional sizing data, inconsistent output formats, and insufficient production guidance information. First, in step S61, by constructing a detailed sizing details table, various data of each group member (including unique identifier, height, weight, template selection, final sizing index, etc.) are recorded in strict order and output as a Comma-Separated Values (CSV) format file, where the first line is a fixed title and the subsequent lines are sorted by member number. This solves the problem of difficult subsequent processing caused by non-standard data formats or chaotic recording orders in the prior art, ensuring accurate, standard data output that is convenient for querying and retrieval. Subsequently, in step S62, the system calculates the number of occurrences of each unique size and generates corresponding production instruction data based on the statistical results, that is, assigns a recommended production quantity to each size. This step effectively solves the problems of unclear production quantities and unscientific production arrangements in traditional production guidance. By automatically generating accurate production instruction data, it not only reduces manual statistical errors but also makes the allocation of production resources more reasonable, helping to improve production efficiency and reduce inventory backlogs. In step S63, the system integrates all key information into a comprehensive production guidance document, which details the sizing details table, group data statistics (such as average size, minimum size, maximum size), recommended production quantities corresponding to each size, and the template mapping functions used, etc. This document provides a comprehensive and standardized guidance document for the production line, enabling the production process to be strictly executed according to the predetermined data, avoiding production deviations caused by incomplete information or inconsistent data, and ensuring the stability of the final product quality and wearing effect. Finally, step S64 clearly stipulates the interface specifications of the output files, ensuring that all generated files are in CSV format and the first line is a fixed field title. This not only facilitates internal data docking within the system but also makes the subsequent interface docking with other production systems or information management platforms simple and efficient. Generally speaking, through these steps, the solution realizes the standardization of data output, the comprehensiveness of production guidance information, and the convenience of interface docking, thus greatly improving the scientific nature and execution efficiency of production guidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Example, referring to Figure 1 , an intelligent size generation method for group clothing based on multi-template dynamic adaptation, includes: Record the height of any member in the group as , and the value range is , with the unit of centimeter; Record the weight of any member in the group as , and the value range is , with the unit of kilogram; Set template , and at the same time set the 5 cm interval template mapping function as ; Set template , and at the same time set the 10 cm interval template mapping function as ; Set template , and at the same time set the comprehensive template mapping function as ; For template , set the threshold set as: ; where each threshold corresponds to a 5 cm interval level; For template , set the threshold set as: ; where each threshold corresponds to a 10 cm interval level; For template , the threshold is directly determined by the value in the formula and does not require a separate threshold set; Collect user data and then perform individual classification; Formulate template mapping and dynamic adaptation rules; Users perform template selection and personalized real-time size fine-tuning; Integrate and statistically generate group size data; Output the final data and production guidance documents.

[0023] The setting of the 5 cm interval template mapping function as , the setting of the 10 cm interval template mapping function as and the setting of the comprehensive template mapping function as , specifically include: Set the 5 cm interval template mapping function as , ; Among them, is the floor function; the denominator 5 represents the fixed interval of height difference; Set the 10 cm interval template mapping function as , ; Among them, the denominator 10 represents the fixed interval of height difference; Set the comprehensive template mapping function as , ; Among them, the denominator 10 is used to unify the mapping scale.

[0024] By first recording the height and weight of any member in the group and fixing their value ranges in the steps, with the height unit in centimeters and the weight unit in kilograms, this solution solves the problems of non-standard data collection and unclear data range in the prior art. Then, by setting a multi-template system, including a 5 cm interval template, a 10 cm interval template, and a comprehensive template, each template is provided with a clear mapping function. Among them, the 5 cm interval template mapping function maps the height to a size index through a fixed formula, solving the problem of rough size division in the traditional method; the 10 cm interval template mapping function is more suitable for clothing adaptation for groups with higher or lower heights through a fixed 10 cm interval; while the comprehensive template mapping function maps by combining the values of height and weight to unify the mapping scale, thus solving the deficiency that a single dimension cannot fully reflect individual body type differences. By setting a threshold set for the template, where the threshold sets for the 5 cm and 10 cm intervals clearly correspond to their respective levels, this solution eliminates the problem of inaccurate size adaptation caused by unclear threshold setting and inflexible parameter adjustment in the traditional technology. At the same time, for the comprehensive template, the threshold is directly determined by the values in the formula without the need to set a separate threshold set, simplifying the system design and ensuring data consistency and standardization. Generally speaking, through standardized data collection, setting clear multi-template mapping functions, and setting strict threshold sets, this solution solves the problems of unreasonable clothing size generation and low individual matching degree in the prior art due to non-standard data, fixed template parameters, and inaccurate threshold setting, significantly improving the fitting accuracy and production efficiency of group clothing sizes, and at the same time providing accurate and standardized data output for subsequent production links, effectively improving the problems of non-uniform sizes and inability to fully reflect individual differences in clothing production, thereby improving the comfort and aesthetics of clothing wearing.

[0025] The described acquisition of user data and then individual classification is specifically as follows: Obtain the height of each group member; Obtain the weight of each group member; Record the data of each group member as follows: ; wherein, is the unique identifier of the th member; is the height of the th member; is the weight of the th member; For the th member, set the reference weight as follows: ; wherein, the denominator 2 is the standard constant for human weight gain, which has been proven applicable to group standards through experiments and is prior art; Set a classification mark to reflect the body type deviation of group members, specifically: ; wherein, is the classification mark of the th member.

[0026] By obtaining the height and weight data of each group member and recording them as standardized data records containing a unique identifier, height and weight, this solution effectively solves the problems of inconsistent data collection standards and unclear data formats in the prior art. First, the height and weight of each member are obtained to ensure that each data item has a fixed value range and unit, thereby avoiding data deviations caused by measurement errors or irregular records. Then, the system formats the data records of each member into records containing a unique identifier (identifying each member), height and weight, so that the entire data set has good structure and traceability, solving the problem of subsequent processing difficulties caused by chaotic data recording methods in traditional systems. On this basis, this solution sets a baseline weight for each group member. This step constructs a standardized baseline weight using height data, providing a clear reference for subsequent individual classification. Through this calculation, the actual weight can be intuitively compared with the baseline weight to determine the body shape deviation of each member. Subsequently, the system further sets a classification mark to qualitatively classify the body shape of each member. The classification mark clearly identifies whether the member is thin, standard or fat based on the comparison result between the member's actual weight and the calculated baseline weight. In this way, the problem of inaccurate size generation caused by the lack of dynamic and clear classification standards in traditional methods has been effectively solved. The introduction of classification marks enables the system to implement refined management based on individual differences, thereby improving the accuracy and personalization of size allocation. Overall, through the four steps of data collection, standardized records, baseline weight calculation, and classification mark setting, this solution solves the problems of non-standard data, inconsistent records, lack of unified reference, and unclear individual body shape judgment in the prior art. This step not only ensures the accurate collection and reasonable recording of each member's data, but also provides a reliable foundation for subsequent size generation and template mapping, ultimately achieving accurate division and dynamic adaptation of group clothing sizes, and improving the overall production and wearing effects.

[0027] The template mapping and dynamic adaptation rule formulation are specifically as follows: Each group member determines the template based on their choice , calculate the preliminary size index, specifically: ; in, For the Each member selects a certain template; , indicating the selection of a template ; , indicating the selection of a template ; , indicating the selection of a template ; For the Preliminary size index of each member; Set a body type correction function to calculate the adjusted size index, specifically as follows: ; Among them, is the adjusted size index for the th member; this operation ensures that the physiological data and body type deviation of each member are accurately mapped to a unique integer size index.

[0028] Through this series of steps of template mapping and dynamic adaptation rule formulation, this solution first requires each group member to select a predefined template type according to their own situation and use the corresponding template mapping function to calculate a preliminary size index. This step solves the problems of fixed templates and inflexible size index calculation in traditional size generation methods. Specifically, by calling the template mapping function, the height of the member or the comprehensive data of height and weight is directly mapped to a preliminary size index, so that each member can obtain a preliminary size value calculated based on a fixed formula. Since each template has clear mapping rules, for example, there are differences in intervals between different templates, the system can perform more refined hierarchical mapping for different body type groups, thus improving the accuracy of size calculation. Next, the solution sets a body type correction function to dynamically adjust the preliminary size index. This function combines the body type classification mark obtained from the previous data collection stage with the preliminary size index and performs mathematical operations to generate an adjusted size index. This process solves the problem in traditional technologies that cannot fully reflect individual body type differences and dynamic deviations. Specifically, the body type correction function can perform addition and subtraction operations on the preliminary size index according to the actual body type deviation of the member, such as being thinner or fatter, so that the finally mapped size can reflect both the physiological parameters and individual body type characteristics of the member, achieving precise quantification of the size index. Overall, through template mapping and dynamic adaptation rule formulation, the solution solves the problems of inaccurate individual size adaptation, lack of flexibility in size index calculation, and insufficient consideration of body type deviation in traditional methods. By adopting the steps of pre-selecting a template and using the mapping function to calculate the preliminary size index, and then combining the body type correction function for adjustment, it is ensured that the physiological data and body type characteristics of each member can be accurately and directly mapped to a unique integer size index. This method not only improves the accuracy of size generation, but also makes the size calculation process operable and adaptable, thus providing standardized and refined production guidance data for subsequent clothing production, effectively improving the defects of uneven traditional size distribution and insufficient adaptability.

[0029] The user makes template selection and personalized real-time size fine-tuning, specifically as follows: In the user operation interface, each member confirms the template type they have selected and the size index ; Set the adjustment range in the size adjustment input box to be , where is the minimum value that can be entered in the size adjustment input box; is the maximum value that can be entered in the size adjustment input box; for example ; Obtain the fine-tuning value entered by the th member in the group through the size adjustment input box ; Calculate the final size index based on user confirmation and fine-tuning, specifically: ; where is the th member's final size index.

[0030] Through a series of steps including template selection and personalized real-time size fine-tuning, this solution enables users to further confirm and adjust the automatically generated size during the actual operation process, thus solving the problem that the size calculation results in traditional size generation methods do not fully match the actual wearing needs of individuals. First, in the user operation interface, each group member needs to confirm the size index calculated by the system in advance based on template mapping and body type correction, and select the corresponding template type. This step ensures that each member can intuitively understand their preliminary size based on physiological data and conduct a preliminary verification, avoiding potential deviations that may occur by simply relying on the automatic calculation results. Secondly, the system sets a size adjustment input box in the interface and clearly stipulates the adjustment range, such as the minimum and maximum values of the input range. On the one hand, this provides a safe and standardized fine-tuning space for users, and on the other hand, it prevents the system from outputting errors due to users entering values outside the reasonable range. Next, the system real-time obtains the fine-tuning values input by each member in the group through the adjustment input box, and accumulates the fine-tuning values with the preliminary size index to calculate the final size index. This calculation process uses direct numerical addition to ensure that all fine-tuning operations are based on clear and fixed values, thus avoiding errors caused by complex models or unclear weight settings in traditional technologies. Through this operation, users can directly affect the final size index according to their own wearing experience, making the size generation have both the high efficiency of automation and the flexibility and accuracy of personalized customization. In summary, by confirming the template type and preliminary size in the user operation interface, setting the value range of the size adjustment input box, real-time obtaining the fine-tuning values, and accumulating the fine-tuning results with the automatically calculated size index, this solution successfully solves the problems that the size calculation results in traditional systems cannot fully reflect individual needs and the lack of personalized customization ability. Finally, this step realizes an accurate size generation process combining automatic calculation and user real-time fine-tuning, which not only improves the accuracy of size data but also enhances the user's sense of participation and customization experience, providing more scientific and standardized production guidance data for subsequent clothing production and ensuring a significant improvement in the fitness and wearing comfort of group clothing.

[0031] The integration and statistical generation of group size data are specifically as follows: For the th member in the group, construct a record, specifically: ; And store all records in the database; Set the group size set as ; Among them, is the total number of group members; Calculate the average size of group members, specifically: ; Among them, is the arithmetic mean of the sizes of group members; Obtain the smallest size among the sizes of group members, specifically: ; Among them, is the smallest size among the sizes of group members; Obtain the largest size among the sizes of group members, specifically: ; Among them, is the largest size among the sizes of group members; For any unique size , set a calculation function to calculate the number of occurrences, specifically: ; Among them, is the count of records that meet the conditions; is the unique size 's count.

[0032] Through a series of steps of integrating and statistically analyzing group size data, this solution first records in detail the data of each member in the group. The data record of each member includes a unique identifier, height, weight, template selection, and the finally generated size index. All data records are stored in a unified database, thus solving the problems of scattered data and inconsistent records in traditional methods. Then, the system sets up a group size set, aggregates the final size indices of all members, and calculates the average size, minimum size, and maximum size of the group members, solving the problems of lack of overall data analysis and inability to reflect the balance of size distribution among group members in traditional methods. Specifically, calculating the average size can intuitively reflect the overall size level, and obtaining the minimum and maximum sizes effectively reveals the differences among individuals, thus providing an intuitive statistical basis for subsequent production. In addition, by setting up a calculation function for any unique size and counting the number of occurrences of each size one by one, this solution solves the problems of inaccurate size statistics and unclear production quantity suggestions in traditional technologies. This calculation function counts the records that meet the conditions to ensure the accuracy and reliability of the production suggestion quantity for each size. Through this series of data integration and statistical steps, the system not only improves the accuracy and standardization of data processing, but also provides accurate size distribution data and production quantity guidance for the production link, thereby effectively reducing the waste of production resources and inventory risks caused by uneven size distribution. Generally speaking, by constructing group data records, storing them in the database, building a size set, calculating the average, minimum, and maximum sizes, and counting the unique sizes, the problems of scattered data collection, inaccurate statistics, and insufficient production guidance data in the existing technology are solved, making the group clothing size allocation more scientific and reasonable, providing a rigorous and reliable basis for clothing production, and ultimately improving the comfort of clothing wearing and the overall production efficiency.

[0033] The output of the final data and production guidance documents is specifically as follows: S61. Generate a final size details table: Construct a size details table, and the content of each row record is: ; The output file adopts the comma-separated value format, and the first row is the title , and then sequentially record according to the numbers of group members from 1 to sort the records; S62. Generate production quantity guidance information: For each unique size , generate production instruction data: ; Record the recommended production quantity corresponding to each size; S63. Compile a comprehensive production guidance document: The document contains: data of size detail table; group data , and ; All sizes The corresponding production quantity ; Template mapping function , and ; S64, data output interface specification: Specifies that the output file format is a CSV file, with the first line containing fixed field headers.

[0034] Through the series of steps of final data output and production guidance file generation, this solution realizes the seamless connection of information from data collection, analysis to production execution, and solves the problems of traditional size data dispersion, non-uniform output format and insufficient production guidance information. First, step S61 records the data of each group member (including unique identifier, height, weight, template selection, final size index, etc.) in a strict order by constructing a detailed size detail table, and outputs it as a comma separated value (CSV) format file, in which the first line is a fixed title and the subsequent lines are sorted by member number. This solves the problem of subsequent processing difficulties caused by non-standard data format or chaotic record order in the prior art, and ensures that the data output is accurate, standard, and easy to query and retrieve. Subsequently, in step S62, the system calculates the number of occurrences of each unique size, and generates corresponding production instruction data based on the statistical results, that is, specifies a recommended production quantity for each size. This step effectively solves the problems of unclear size production quantity and unscientific production arrangement in traditional production guidance. By automatically generating accurate production instruction data, it not only reduces manual statistical errors, but also makes production resource allocation more reasonable, which helps to improve production efficiency and reduce inventory backlog. In step S63, the system integrates all key information into a comprehensive production guidance document, which contains detailed size details, group data statistics (such as average size, minimum size, maximum size), recommended production quantity corresponding to each size, and template mapping function used. This document provides a comprehensive and standardized guidance document for the production line, so that the production process can be strictly executed according to the predetermined data, avoiding production deviations caused by incomplete information or inconsistent data, and ensuring the stability of the final product quality and wearing effect. Finally, step S64 clearly specifies the interface specifications of the output file, ensuring that all generated files are in CSV format and the first line is a fixed field title, which is not only conducive to the internal data docking of the system, but also makes the subsequent interface docking with other production systems or information management platforms simple and efficient. In general, through these steps, the solution achieves standardization of data output, comprehensiveness of production guidance information, and convenience of interface docking, thereby greatly improving the scientific nature and execution efficiency of production guidance.

[0035] This embodiment also provides a system for a group clothing intelligent size generation method based on multi-template dynamic adaptation, including: Template management module, used to set templates and template mapping; Rule engine module, used for classification and labeling of height and weight data; Dynamic interaction module, used for users to input fine-tuning values and select template type and size display; Data export module for generating final size chart and exporting production data.

[0036] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0037] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An intelligent size generation method for group clothing based on multi-template dynamic adaptation, characterized in that Include: Record the height of any member in the group as , and the value range is , with the unit of centimeter; Record the weight of any member in the group as , with a value range of , and the unit is kilograms; Set template , and set the template mapping function with a 5 cm interval as ; Set template , and set the template mapping function with a 10 cm interval to be ; Set template , and set the comprehensive template mapping function as ; For the template set the threshold set as: ; where each threshold corresponds to a 5-cm interval level For the template , set the threshold set as follows: ; where each threshold corresponds to a 10-cm interval level Collect user data and then conduct individual classification; Formulate template mapping and dynamic adaptation rules; The user selects a template and makes real-time personalized size fine-tuning; Integrate and statistically generate group size data; Output the final data and production guidance documents.

2. The intelligent size generation method for group clothing based on multi-template dynamic adaptation according to claim 1, wherein The set 5 cm interval template mapping function is , the set 10 cm interval template mapping function is and the set comprehensive template mapping function is , specifically including: Set the 5-cm interval template mapping function to , ; Among them, is for rounding down; the denominator 5 represents the fixed interval of height difference; Set the 10 cm interval template mapping function to , ; Among them, the denominator 10 represents the fixed interval of height difference; Set the comprehensive template mapping function to , ; Among them, the denominator 10 is used to unify the mapping scale.

3. The intelligent size generation method for group clothing based on multi-template dynamic adaptation according to claim 2, wherein, The step of collecting user data and then conducting individual classification is specifically as follows: Obtain the height of each group member; Obtain the weight of each group member; Record the data of each group member as: ; Among them, is the unique identifier of the th member; is the height of the th member; is the weight of the th member; For the th member, set the reference weight , specifically: ； Among them, the denominator 2 is the standard constant for human weight gain; Set a classification mark to reflect the body type deviation of group members, specifically: ； Among them, is the classification mark of the th member.

4. The intelligent size generation method for group clothing based on multi-template dynamic adaptation according to claim 3, wherein, The step of formulating template mapping and dynamic adaptation rules is specifically as follows: Each group member determines a preliminary size index according to the template selected by him / her, specifically as follows: , specifically as follows: ； Among them, is the template determined for the th member; , indicating the selection of template ; , indicating the selection of template ; , indicating the selection of template ; is the preliminary size index of the th member; Set a body type correction function to calculate the adjusted size index, specifically: ； Among them, is the sized index after adjustment for the 5. The intelligent size generation method for group clothing based on multi-template dynamic adaptation according to claim 4, characterized in that, The step of the user selecting a template and making real-time personalized size fine-tuning is specifically as follows: In the user operation interface, each member confirms the template type and size index he / she has selected and size index ; Set the adjustment range in the size adjustment input box to be , where is the minimum value that can be entered in the size adjustment input box; is the maximum value that can be entered in the size adjustment input box; Obtain the fine-tuning value input by the th member in the group through the size adjustment input box ; Calculate the final size index according to user confirmation and fine-tuning, specifically: ； Among them, is the final size index of the 6. The intelligent size generation method for group clothing based on multi-template dynamic adaptation according to claim 5, wherein The step of integrating and statistically generating group size data is specifically as follows: For the th member in the group, construct a record, specifically: ； And store all records in the database; Set the group size set to ; Among them, is the total number of group members; Calculate the average size of the group members, specifically as follows: ; Among them, is the arithmetic mean of the group member sizes; Obtain the minimum size among the group members' sizes, specifically: ; wherein, is the smallest size among the group member sizes; Obtain the maximum size among the group members' sizes, specifically: ; wherein, is the largest size among the group member sizes; For any unique size , set a calculation function to calculate the number of occurrences, specifically: ； Among them, is to count the records that meet the conditions; is the unique size count.

7. The intelligent size generation method for group clothing based on multi-template dynamic adaptation according to claim 6, wherein The step of outputting the final data and production guidance documents is specifically as follows: S61. Generate a final size details table: Construct a detailed form for sizes, with each row recording the following content: ; The output file is in comma-separated value format, and the first line is the title , and then followed by the numbers of the group members from 1 to sort and record; S62. Generate production quantity guidance information: For each unique size , generate production instruction data: ； Record the recommended production quantity corresponding to each size; S63. Compile a comprehensive production guidance document: The document contains: data of the size details table; group data , and ; production quantity corresponding to each size ; template mapping functions ; template mapping functions , and ; S64. Data output interface specification: Specify that the output file format is a CSV file, and the first line is the fixed field title.

8. A system using the method for intelligently generating group clothing sizes based on multi-template dynamic adaptation described in claim 7, characterized in that, Include: A template management module for setting templates and template mapping; A rule engine module for classifying and marking height and weight data; A dynamic interaction module for the user to input fine-tuning values and display the selection of template types and sizes; A data export module for generating a final size table and outputting production data.

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